1. Field of the Invention
The invention concerns a method to extract at least one diffusion direction from diffusion-weighted magnetic resonance signals (or DW-MR images for Diffusion-Weighted Magnetic Resonance images) of any part of a body that contains water molecules, comprising
exposing the body to a magnetic resonance imaging process, in which at least one magnetic field gradient is applied to the entire body part, at least one magnetic resonance signal is measured in each voxel of said body part and at least one diffusion direction is determined based on said magnetic resonance signal.
Various methods to extract diffusion directions from DW-MRI data are known in the prior art.
2. Description of Related Art
Two main families of methods are known: on the one hand methods using diffusion tensor imaging DTI according to documents [1], [2] and [3], and on the other hand methods using high angular resolution diffusion imaging NARDI according to documents [4], [5] and [6].
Document [1] U.S. Pat. No. 5,539,310 describes a magnetic resonance spectroscopy system comprising: means for applying a static magnetic field to an object under examination; means for applying magnetic field gradients to the object; means for applying a high frequency pulse to the object; detecting means for detecting a magnetic resonance signal from the object; control means for ordering said means to apply magnetic field gradients, and means for applying a high frequency pulse to operate in a predetermined sequence so as to induce magnetic resonance within the object, wherein said control means includes means for generating a plurality of echo signals corresponding to non-collinear directions of said magnetic field gradients; and means for processing data based on the magnetic resonance signal detected by said detecting means to obtain magnetic resonance information, wherein said means for processing data provides an expression of each said echo signal in terms of a diffusion tensor that summarizes the displacements of water molecules due to the diffusion phenomenon primed by said magnetic field gradients.
Document [1] also provides an estimation of the diffusion tensor.
Document [2] US-2005/0007100 proposes an alternative method for the estimation of the diffusion tensor and furthermore q-space MRI specimen characterization. In q-space MRI, a sample portion is situated in a static magnetic field, typically for a duration long enough to permit some or all spins of one or more species like for example water molecules in the sample portion to align with the static magnetic field. It provides another method to estimate the diffusion tensor from DW-MR images, comprising: obtaining a set of diffusion-weighted magnetic resonance signals associated to a plurality of both magnetic field gradients strengths and directions; and estimating the values of the elements of the tensor D by application of a series of gradient pulses in different directions. According to the same, magnetic resonance methods include modelling magnetic resonance signals obtained from specimens at low and high q-values to obtain parameters associated with specimen structure (wherein brain white matter is a representative anisotropic specimen) and orientation. In evaluation of brain white matter specimens, diffusion within axons is modelled as restricted diffusion along the axon axis according to Neumann's model and as hindered diffusion in the plane perpendicular to the axon axis according to the Gaussian model.
Document [3] U.S. Pat. No. 6,526,305 concerns a method of fiber reconstruction employing data acquired by magnetic resonance imaging. A method of creating an image of brain white matter fibers includes exposing the brain white matter fibers to a magnetic resonance imaging process. The data acquisition from the magnetic resonance imaging includes the acquisition of diffusion-weighted magnetic resonance images that are later employed to calculate an apparent diffusion coefficient in each voxel. The data is introduced into a microprocessor which calculates the six parameters of a three-dimensional diffusion tensor and deduces its three eigenvalues and its three eigenvectors. These eigenvalues along with their associated eigenvectors are subjected to further microprocessing to generate a plurality of images representing a plurality of diffusion properties of the fibers. The process in a preferred embodiment includes that the input of an initiation pixel begins the reconstruction process which involves pixel connecting and ultimately judgement or a decision based upon the tracking reaching termination of the fiber in each direction. If comparison in the computer results in the conclusion that the end of the fiber has been reached (“yes”), that is the end of the process as to that axonal fiber. If not (“no”), the fiber reconstruction process continues. A method of creating an image of individual brain white matter fibers is provided comprising exposing said brain white matter fibers to a DW-MR imaging process, introducing data acquired from said magnetic resonance imaging process into a computer, estimating a diffusion tensor to obtain a plurality of scalar values and a plurality of vectors from said data, employing said values and said vectors to initiate brain fiber tracking at a pixel of interest, and continuing or terminating said fiber tracking based upon a determination regarding whether the fiber is continuous or terminated based on randomness of fiber orientation of adjacent pixels.
One problem of DTI-based methods using estimation of the diffusion tensor according to documents [1], [2] and [3] is that they do not permit to detect multiple fibres when the fibres are crossing. Another inconvenience of DTI is that it provides a had estimation of the diffusion direction when the DW-MR images are too noisy.
Document [4] US-2008/0252291 concerns a High Angular Resolution Diffusion Weighted MRI (HARDI). A magnetic resonance imaging method involves acquisition of magnetic resonance signals with application of diffusion weighting at a plurality of diffusion weighting strengths and a plurality of diffusion directions. The occurrence of one single or several diffusion directions is identified for individual voxels. In this way account is taken of crossing fibres. However, the identification of crossing of fibres is detrimental to the acquisition load and time spent, because it requires a large amount of magnetic resonance (MRI) signals resulting from the application of a huge number of MR gradients.
Document [5] “Von Mises-Fisher Mixture Model of the diffusion ODF”, Tim Mc Graw, Baba C. Vemuri, Bob Yeziersky, Thomas Mareci, concerns high angular resolution diffusion imaging (HARDI) that allows the computation of water molecules displacements probabilities in the whole three-dimensional space. This probability function is referred to as the orientation distribution function ODF which is known as the reverse Fourier transform of the DW-MR image. The ODF over the sphere is obtained by integrating the displacements probability over the radial component. The latter ODF over the sphere is modelled by means of a mixture of von Mises-Fisher distributions.
There is a high loss of information in the method of document [5], because two digital integrations are necessary. This high loss of information can only be compensated by making a greater number of MRI acquisitions, which are then more time-consuming. Furthermore, the ODF is sampled over a set of arbitrary directions and requires a learning algorithm, which makes it not easily and systematically able to be generalised to every clinical cases.
Document [6] “Hyperspherical von Mises-Fisher Mixture (HvMF) Modelling of High Angular Resolution Diffusion MRI”, Ahhir Rhalerao, Carl-Fredrik Westin, MICCAI 2007, Part 1, LNCS 4791, pp 236-243, 2007, describes a mapping of unit vectors onto a 5D hypersphere to model and partition ODFs from HARDI data and makes a link to interpretation of the second order spherical harmonic decompositions of HARDI data.
The problem of techniques in documents [4], [5] and [6] using HARDI is that these methods require the acquisition of a large amount of magnetic resonance (MRI) signals resulting from the application of a huge number of MR gradients. A lower bound of this number is nowadays around 60, leading to a DW-MRI sequence of about 10 to 12 minutes. It is thus not easily applicable to clinical routine where lengthy acquisitions are often not possible on patients suffering from severe pathologies (e.g. psychiatry, multiple sclerosis, Parkinsonian disorders, paediatrics).